Anomaly Detection by Learning Dynamics From a Graph
Citations Over Time
Abstract
There exist relations, which vary with time or by an event, between high dimensional elements in most real-world datasets. A dynamic graph or network has been used as one of the remarkable approaches to represent and analyze them. In spite of the advantages of representing data in the form of graphs, it is difficult to apply representation (deep) learning to graphs. Recently, AlphaFold by DeepMind has shown remarkable results in applying deep learning to graphs. This research is part of the current effort to extend the input domain of deep learning to arbitrarily graphs and their dynamics of variations. In this paper, we propose a method to predict the evolution of graphs by learning spatio-temporal features called dynamics. The method involves two main processes: extracting spatial features from static graphs obtained at different times and learning temporal features from the time-varying connection structure. Instead of predicting the overall changes of a highly complex graph, we detect the dynamic anomaly by predicting the affinity score with respect to a node (e.g., a hub as an important factor) of a dynamics graph. This facilitates the learning dynamics of graphs having sparsity of connections by alleviating the curse of dimensions using the fact that most graphs of real-world problems are scale-free. To justify our approach, we apply our method to real-world problems such as computer networks and public transportation. Experimental results show that our approach is competitive with other existing methods.
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